Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index
Background: Determining the DNA fragmentation index (DFI) by the sperm chromatin dispersion (SCD) test involves manual counting of stained sperms with halo and no halo. Aims: The aim of this study is to build a robust artificial intelligence-based solution to predict the DFI. Settings and Design: Th...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2023-01-01
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| Series: | Journal of Human Reproductive Sciences |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.4103/jhrs.jhrs_4_23 |
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| _version_ | 1823860942538014720 |
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| author | Roopini Sathiasai Kumar Swapnil Sharma Arunima Halder Vipin Gupta |
| author_facet | Roopini Sathiasai Kumar Swapnil Sharma Arunima Halder Vipin Gupta |
| author_sort | Roopini Sathiasai Kumar |
| collection | DOAJ |
| description | Background:
Determining the DNA fragmentation index (DFI) by the sperm chromatin dispersion (SCD) test involves manual counting of stained sperms with halo and no halo.
Aims:
The aim of this study is to build a robust artificial intelligence-based solution to predict the DFI.
Settings and Design:
This is a retrospective experimental study conducted in a secondary in vitro fertilisation setup.
Materials and Methods:
We obtained 24,415 images from 30 patients after the SCD test using a phase-contrast microscope. We classified the dataset into two, binary (halo/no halo) and multiclass (big/medium/small halo/degraded (DEG)/dust). Our approach consists of a training and prediction phase. The 30 patients' images were divided into training (24) and prediction (6) sets. A pre-processing method M was developed to automatically segment the images to detect sperm-like regions and was annotated by three embryologists.
Statistical Analysis Used:
To interpret the findings, the precision-recall curve and F1 score were utilised.
Results:
Binary and multiclass datasets containing 8887 and 15,528 cropped sperm image regions showed an accuracy of 80.15% versus 75.25%. A precision-recall curve was determined and the binary and multiclass datasets obtained an F1 score of 0.81 versus 0.72. A confusion matrix was applied for predicted and actuals for the multiclass approach where small halo and medium halo confusion were found to be highest.
Conclusion:
Our proposed machine learning model can standardise and aid in arriving at accurate results without using expensive software. It provides accurate information about healthy and DEG sperms in a given sample, thereby attaining better clinical outcomes. The binary approach performed better with our model than the multiclass approach. However, the multiclass approach can highlight the distribution of fragmented and non-fragmented sperms. |
| format | Article |
| id | doaj-art-236cccb9e9a04c0bbd5bac2198bf03c9 |
| institution | Kabale University |
| issn | 0974-1208 1998-4766 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Human Reproductive Sciences |
| spelling | doaj-art-236cccb9e9a04c0bbd5bac2198bf03c92025-02-10T07:37:14ZengWolters Kluwer Medknow PublicationsJournal of Human Reproductive Sciences0974-12081998-47662023-01-01161162110.4103/jhrs.jhrs_4_23Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation IndexRoopini Sathiasai KumarSwapnil SharmaArunima HalderVipin GuptaBackground: Determining the DNA fragmentation index (DFI) by the sperm chromatin dispersion (SCD) test involves manual counting of stained sperms with halo and no halo. Aims: The aim of this study is to build a robust artificial intelligence-based solution to predict the DFI. Settings and Design: This is a retrospective experimental study conducted in a secondary in vitro fertilisation setup. Materials and Methods: We obtained 24,415 images from 30 patients after the SCD test using a phase-contrast microscope. We classified the dataset into two, binary (halo/no halo) and multiclass (big/medium/small halo/degraded (DEG)/dust). Our approach consists of a training and prediction phase. The 30 patients' images were divided into training (24) and prediction (6) sets. A pre-processing method M was developed to automatically segment the images to detect sperm-like regions and was annotated by three embryologists. Statistical Analysis Used: To interpret the findings, the precision-recall curve and F1 score were utilised. Results: Binary and multiclass datasets containing 8887 and 15,528 cropped sperm image regions showed an accuracy of 80.15% versus 75.25%. A precision-recall curve was determined and the binary and multiclass datasets obtained an F1 score of 0.81 versus 0.72. A confusion matrix was applied for predicted and actuals for the multiclass approach where small halo and medium halo confusion were found to be highest. Conclusion: Our proposed machine learning model can standardise and aid in arriving at accurate results without using expensive software. It provides accurate information about healthy and DEG sperms in a given sample, thereby attaining better clinical outcomes. The binary approach performed better with our model than the multiclass approach. However, the multiclass approach can highlight the distribution of fragmented and non-fragmented sperms.https://journals.lww.com/10.4103/jhrs.jhrs_4_23clinical outcomedna fragmentation indexmachine learning |
| spellingShingle | Roopini Sathiasai Kumar Swapnil Sharma Arunima Halder Vipin Gupta Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index Journal of Human Reproductive Sciences clinical outcome dna fragmentation index machine learning |
| title | Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index |
| title_full | Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index |
| title_fullStr | Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index |
| title_full_unstemmed | Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index |
| title_short | Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index |
| title_sort | deep learning based robust automated system for predicting human sperm dna fragmentation index |
| topic | clinical outcome dna fragmentation index machine learning |
| url | https://journals.lww.com/10.4103/jhrs.jhrs_4_23 |
| work_keys_str_mv | AT roopinisathiasaikumar deeplearningbasedrobustautomatedsystemforpredictinghumanspermdnafragmentationindex AT swapnilsharma deeplearningbasedrobustautomatedsystemforpredictinghumanspermdnafragmentationindex AT arunimahalder deeplearningbasedrobustautomatedsystemforpredictinghumanspermdnafragmentationindex AT vipingupta deeplearningbasedrobustautomatedsystemforpredictinghumanspermdnafragmentationindex |